Discrete Choice Experiment Design Research Articles

What Do People Want from an AI-Assisted Screening App for Sexually Transmitted Infection-Related Anogenital Lesions: A Discrete Choice Experiment.

One of the World Health Organization (WHO) recommendations to achieve its global targets for sexually transmitted infections (STIs) is the increased use of digital technologies. Melbourne Sexual Health Centre (MSHC) has developed an AI-assisted screening application (app) called AiSTi for the detection of common STI-related anogenital skin conditions. This study aims to understand the community's preference for using the AiSTi app. We used a discrete choice experiment (DCE) to understand community preferences regarding the attributes of the AiSTi app for checking anogenital skin lesions. The DCE design included the attributes: data type; AI accuracy; verification of result by clinician; details of result; speed; professional support; and cost. The anonymous DCE survey was distributed to clients attending MSHC and through social media channels in Australia between January and March 2024. Participant preferences on various app attributes were examined using random parameters logit (RPL) and latent class analysis (LCA) models. The median age of 411 participants was 32 years (interquartile range 26-40 years), with 64% assigned male at birth. Of the participants, 177 (43.1%) identified as same-sex attracted and 137 (33.3%) as heterosexual. In the RPL model, the most influential attribute was the cost of using the app (24.1%), followed by the clinician's verification of results (20.4%), the AI accuracy (19.5%) and the speed of receiving the result (19.1%). The LCA identified two distinct groups: 'all-rounders' (88%), who considered every attribute as important, and a 'cost-focussed' group (12%), who mainly focussed on the price. On the basis of the currently available app attributes, the predicted uptake was 72%. In the short term, a more feasible scenario of improving AI accuracy to 80-89% with clinician verification at a $5 cost could increase uptake to 90%. A long-term optimistic scenario with AI accuracy over 95%, no clinician verification and no cost could increase it to 95%. Preferences for an AI-assisted screening app targeting STI-related anogenital skin lesions are one that is low-cost, clinician-verified, highly accurate and provides results rapidly. An app with these key qualities would substantially improve user uptake.

Assessing Student Preferences for Quantitative Methods Courses Using Discrete Choice Experiments

Understanding how students choose quantitative methods courses is important for both instructors and departments: Meeting student preferences increases motivation and engagement, enhances learning, can contribute to reducing statistics anxiety, and ensures enrollment. This article uses a discrete choice experiment to causally assess sociology students’ course preferences. In contrast to existing experimental interventions, choice experiments allow for an estimation of treatment effects prior to implementing an instructional design. Using data collected at a German university as a case study, this contribution provides insights and discusses practical implications of student preferences for quantitative methods courses. In the study, students prefer courses that use hands-on forms of assessment, follow a regular schedule, offer more credit points, and mix undergraduate and graduate students. The accompanying online material provides a step-by-step introduction to the design of discrete choice experiments, allowing a replication of the study and an implementation of the methodology in other contexts.

Preferences for Delivery of HIV Prevention Services Among Healthcare Users in South Africa: A Discrete Choice Experiment.

Progress has been made to scale oral pre-exposure prophylaxis (PrEP) as part of combination HIV prevention, with the WHO recommending differentiated, simplified and demedicalized approaches. This study explored user preferences for components of a PrEP service delivery package, through a discrete choice experiment (DCE) among 307 people accessing primary healthcare services in South Africa between November 2022 and February 2023. Attributes included were: Source of information about HIV prevention and PrEP; Site for PrEP initiation and follow-up; Frequency of follow-up; PrEP pick-up point; HIV testing whilst using PrEP; Contact between appointments. A D-efficient DCE design was created with 16 choice sets in 2 blocks. DCE data were analysed using generalised multinomial logistic models. Compared to printed materials, participants preferred getting information about PrEP online (aOR = 7.73, 95% CI = 5.13-11.66) and through WhatsApp (aOR = 2.23, 95% CI = 0.98-5.55). PrEP initiation at a pharmacy or mobile clinic was valued equally to initiating PrEP at a clinic, but a community pop-up site was less preferred (aOR = 0.46, 95% CI = 0.33-0.64). There was a preference for 6-monthly over 3-monthly follow-up (aOR = 11.88, 95% CI = 5.44-25.94). Participants preferred collecting PrEP from a pharmacy (aOR = 5.02, 95% CI = 3.45-7.31), through home delivery (aOR = 2.18, 95% CI = 1.26-3.78) and from a vending machine (aOR = 1.43, 95% CI = 1.02-1.99) relative to where they initiated PrEP. Participants also preferred HIV self-testing over a healthcare provider test (aOR = 5.57, 95% CI = 3.72, 8.36). WhatsApp or Facebook groups (aOR = 4.12, 95% CI = 3.00-5.67), monthly phone calls (aOR = 2.84, 95% CI = 1.73-4.67) and weekly messages (aOR = 1.47, 95% CI = 1.10-1.97) were preferred contact between appointments, relative to no contact. To meet users' preferences, there is a need to expand decentralised and self-led HIV prevention services.

A New and Improved Experimental Design for the Discrete Choice Experiment Module of the EuroQol Valuation Technology Protocol

ObjectivesThe EuroQol Valuation Technology (EQ-VT) protocol, which is used to value the EQ-5D-5L instrument, comprises a composite time trade-off and a discrete choice experiment (DCE) module. Despite significant limitations, the DCE module has not been updated since its inception in 2012. This study aimed to update the EQ-VT DCE design using state-of-the-art methods. MethodsDCE data from 19 EQ-5D-5L valuation studies were summarized using a Bayesian hierarchical meta-analysis model, which created the priors for our Bayesian efficient DCE design. This design comprised 20 subdesigns, each with 12 choice tasks, and included 2 levels that overlapped to reduce the complexity of the choice tasks. The relative efficiency and robustness of the new design were established by comparing the D-errors and minimal sample size requirements for the 19 within-sample and 7 out-of-sample countries with the previous DCE design. ResultsThe updated DCE design shows large reductions in the D-error: by 20% and 22% for the 19 within-sample and 7 out-of-sample countries, respectively. Sample size requirements were also reduced, resulting in an average reduction of 45% for both the within and out-of-sample countries. ConclusionsThe updated DCE design outperforms the current EQ-VT design. Given its enhanced performance and reduced complexity, it is set to replace the existing DCE design in future EQ-5D-5L valuation studies using the EQ-VT protocol.

Information about life expectancy related to obesity is most important to cat owners when deciding whether to act on a veterinarian's weight loss recommendation.

To determine the relative importance of information communicated to cat owners during veterinarian-client obesity-related conversations. Cat owner participants recruited via snowball sampling. A cross-sectional online questionnaire was distributed to cat owners who owned cats of any weight status. A discrete choice experiment design was used to determine the relative importance of obesity-related attributes to cat owners when receiving information from a veterinarian. A total of 1,095 questionnaires were analyzed. Participating cat owners resided primarily in Canada and the US. Impact on life expectancy was the most important attribute that would encourage participants to pursue weight management for a cat with obesity (relative importance, 32.66%), followed by change to cost of food (20.40%), future quality of life (20.38%), future mobility (14.40%), and risk of developing diabetes (12.15%). Findings suggest that cat owners consider the impact on life expectancy to be most important when considering whether to follow a veterinarian's recommendation for their cat to lose weight. When veterinary professionals are communicating about obesity in practice, there is the potential to increase owner engagement in weight management efforts for cats by emphasizing the obesity-related information owners prefer to receive.

Nurses’ preferences regarding MenACWY conjugate vaccines attributes: a discrete choice experiment in Spain

ObjectivesImmunisation against preventable diseases as meningitis is crucial from a public health perspective to face challenges posed by these infections. Nurses hold a great responsibility for these programs, which highlights the importance of understanding their preferences and needs to improve the success of campaigns. This study aimed to investigate nurses' preferences regarding Meningococcus A, C, W, and Y (MenACWY) conjugate vaccines commercialised in Spain. Study designA national-level discrete choice experiment (DCE) was conducted. MethodsA literature review and a focus group informed the DCE design. Six attributes were included: pharmaceutical form, coadministration evidence, shelf-life, package contents, single-doses per package, and package volume. Conditional logit models quantified preferences and relative importance (RI). ResultsThirty experienced primary care nurses participated in this study. Evidence of coadministration with other vaccines was the most important attribute (RI = 43.78%), followed by package size (RI = 22.17%), pharmaceutical form (RI = 19.07%), and package content (RI = 11.80%). There was a preference for evidence of coadministration with routine vaccines (odds ratio [OR] = 2.579, 95% confidence interval [95%CI] = 2.210–3.002), smaller volumes (OR = 1.494, 95%CI = 1.264–1.767), liquid formulations (OR = 1.283, 95%CI = 1.108–1.486) and package contents including only vial/s (OR = 1.283, 95%CI = 1.108–1.486). No statistical evidence was found for the remaining attributes. ConclusionsEvidence of coadministration with routine vaccines, easy-to-store packages, and fully liquid formulations were drivers of nurses’ preferences regarding MenACWY conjugate vaccines. These findings provide valuable insights for decision-makers to optimize current campaigns.

Pretesting Discrete-Choice Experiments: A Guide for Researchers.

Discrete-choice experiments (DCEs) are a frequently used method to explore the preferences of patients and other decision-makers in health. Pretesting is an essential stage in the design of a high-quality choice experiment and involves engaging with representatives of the target population to improve the readability, presentation, and structure of the preference instrument. The goal of pretesting in DCEs is to improve the validity, reliability, and relevance of the survey, while decreasing sources of bias, burden, and error associated with preference elicitation, data collection, and interpretation of the data. Despite its value to inform DCE design, pretesting lacks documented good practices or clearly reportedapplied examples. The purpose of this paper is: (1) to define pretesting and describe the pretesting process specifically in the context of a DCE, (2) to present a practical guide and pretesting interview discussion template for researchers looking to conduct a rigorous pretest of a DCE, and (3) to provide an illustrative example of how these resources were operationalized to inform the design of a complex DCE aimed at eliciting tradeoffs between personal privacy and societal benefit in the context of a police method known as investigative genetic genealogy (IGG).

Comparing Preferences for Disease Profiles: A Discrete Choice Experiment from a US Societal Perspective.

There is increasing interest in expanding the elements of value to be considered when making health policy decisions. To help inform value frameworks, this study quantified preferences for disease attributes in a general public sample and examined which combination of attributes (disease profiles) are considered most important for research and treatment. A discrete choice experiment (DCE) was conducted in a US general population sample, recruited through online consumer panels. Respondents were asked to select one of a set of health conditions they believed to be most important, characterized by attributes defined by a previous qualitative study: onset age; cause of disease; life expectancy; caregiver requirement; symptom burden (characterized by the Health Utilities Index with varying levels of ambulation independence, dexterity limitations, and degree of pain and discomfort); and disease prevalence. A fractional factorial DCE design was implemented using R, and 60 choice sets were generated (separated into blocks of 10 per participant). Data were analyzed using a mixed-logit regression model, and results used to assess the likelihood of preferring disease profiles. Based on individual attribute preferences, overall preferences for disease profiles, including a profile aligned with Duchenne muscular dystrophy (DMD), were compared. Fifty-two percent of respondents (n = 537) were female, and 70.6% were aged 18-54 years. Attributes considered most important were those related to life expectancy (odds ratio [OR], 95% confidence interval [CI] 1.88 [1.56-2.27] for a 50% reduction in remaining life expectancy vs no impact), and symptom burden (OR [95% CI] 1.84 [1.47-2.31] for severe vs mild burden). Greater importance was also found for pediatric onset, caregiver requirement, and diseases affecting more people. As an example of disease profile preferences, a DMD-like pediatric inherited disease with 50% reduction in life expectancy, extensive caregiver requirement, severe symptom burden, and 1:5000 prevalence had 2.37-fold higher odds of being selected as important versus an equivalent disease with adult onset and no life expectancy reduction. Of disease attributes included in this DCE, respondents valued higher prevalence of disease, life expectancy and symptom burden as most important for prioritizing research and treatment. Based on expressed attribute preferences, a case study of an inherited pediatric disease involving substantial reductions to length and quality of life and requiring caregiver support has relatively high odds of being identified as important compared to diseases reflecting differing attribute profiles. These findings can help inform expansions of value frameworks by identifying important attributes from the societal perspective.

Estimating a Preference-Based Value Set for the Mental Health Quality of Life Questionnaire (MHQoL).

Health economic evaluations using common health-related quality of life measures may fall short in adequately measuring and valuing the benefits of mental health care interventions. The Mental Health Quality of Life questionnaire (MHQoL) is a standardized, self-administered mental health-related quality of life instrument covering 7 dimensions known to be relevant across and valued highly by people with mental health problems. The aim of this study was to derive a Dutch value set for the MHQoL to facilitate its use in cost-utility analyses. The value set was estimated using a discrete choice experiment (DCE) with duration that accommodated nonlinear time preferences. The DCE was embedded in a web-based self-complete survey and administered to a representative sample (N = 1,308) of the Dutch adult population. The matched pairwise choice tasks were created using a Bayesian heterogeneous D-efficient design. The overall DCE design comprised 10 different subdesigns, with each subdesign containing 15 matched pairwise choice tasks. Each participant was asked to complete 1 of the subdesigns to which they were randomly assigned. The obtained coefficients indicated that "physical health,""mood," and "relationships" were the most important dimensions. All coefficients were in the expected direction and reflected the monotonic structure of the MHQoL, except for level 2 of the dimension "future." The predicted values for the MHQoL ranged from -0.741 for the worst state to 1 for the best state. This study derived a Dutch value set for the recently introduced MHQoL. This value set allows for the generation of an index value for all MHQoL states on a QALY scale and may hence be used in Dutch cost-utility analyses of mental healthcare interventions. A discrete choice experiment was used to derive a Dutch value set for the MHQoL.This allows the use of the MHQoL in Dutch cost-utility analyses.The dimensions physical health, mood, and relationships were the most important.The utility values range from -0.741 for the worst state to 1 for the best state.

Unlocking transparency in water information: a discrete choice experiment design

ABSTRACT This study employed the discrete choice experiment (DCE) to examine consumers’ willingness to gain access to public information on water quality and the tradeoffs. We estimated that if water quality information is provided monthly, weekly, and daily, consumers are willing to pay an additional 28, 29, and 37 thousand VND per cubic metre, respectively. Consumers highly value the transparency of water quality information. High-income consumers are willing to pay more for water quality improvements and transparent water quality information. Further, having safe filter to drink water and potable tap water rather than unsafe drinking water equals an additional 5 and 7 thousand VND per cubic metre in customers’ willingness to pay. Consumers also prefer state-controlled water companies to private companies. In the case of private water companies, consumers also expect these companies to have a high level of awareness about their corporate social responsibility. The findings provide evidence that information transparency in water quality can be a powerful tool that fosters public awareness and holds water companies accountable. Consumers are willing to pay to get access to information on water quality regularly. Since technology has evolved, a digital smart monitoring solution is no longer an option but a potential or a must.

Construction of symmetric paired choice experiments: minimising runs and maximising efficiency

Discrete choice experiments (DCEs) are popular in various fields such as health resources, marketing, transport, economics, and many others for identifying the factors that influence an individual’s choice behaviour. Selecting the DCE design is crucial in determining the observable effects. In this paper, the optimal form of the information matrix is introduced for attributes at two levels, main effect models, and equal choice probabilities for paired choice experiments. Additionally, the construction of D-optimal designs is modified to obtain DCEs when the number of attributes equals the number of runs, including designs with choice sets of sizes that are not necessarily multiples of 4, i.e. N ≢ 0mod4. The designs suggested in this paper have the same or higher D-efficiencies than existing efficient designs for the same number of choice sets. Moreover, the proposed design techniques can be extended to be applied to situations where the attributes of DCEs have a higher number of levels (ℓ > 2), resulting in designs with the same improved D-efficiencies and sufficiently small sample sizes. The designs proposed in this paper offer a notable advantage by allowing a reduction of 33% in the number of choice pairs with only a marginal loss of 11% in D-efficiency when compared to an optimal design. In comparison, the design suggested by other researchers incurs a higher loss in D-efficiency.

Preferences in the Design and Delivery of Neurodevelopmental Follow-Up Care for Children: A Systematic Review of Discrete Choice Experiments.

Neurodevelopmental disorders are a significant cause of morbidity. Early detection of neurodevelopmental delay is essential for timely diagnosis and intervention, and it is therefore important to understand the preferences of parents and clinicians for engaging with neurodevelopmental surveillance and follow-up care. Discrete choice experiment (DCE) may be an appropriate method for quantifying these preferences. This review systematically examined how DCEs have been designed and delivered in studies examining neurodevelopmental care of children and identified the preferred attributes that have been reported. PubMed, Embase, CINAHL, and Scopus databases were systematically searched. Studies were included if they used DCE to elicit preferences for a neurodevelopmental follow-up program for children. Two independent reviewers conducted the title and abstract and full-text screening. Risk of bias was assessed using a DCE-specific checklist. Findings were presented using a narrative synthesis. A total of 6618 records were identified and 16 papers were included. Orthogonal (n=5) and efficient (n=5) experimental designs were common. There was inconsistent reporting of design-related features. Analysis was primarily completed using mixed logit (n=6) or multinomial logit (n=3) models. Several key attributes for neurodevelopmental follow-up care were identified including social, behavioral and emotional support, therapy, waiting time, and out-of-pocket costs. DCE has been successfully used as a preference elicitation method for neurodevelopmental-related care. There is scope for improvement in the design and analysis of DCE in this field. Nonetheless, attributes identified in these studies are likely to be important considerations in the design and implementation of programs for neurodevelopmental care.

Lifestyle interventions for patients with non-alcoholic steato-hepatitis-Design, rationale and protocol of the study "target group-specific optimisation of lifestyle interventions for behavior change in non-alcoholic steato-hepatitis (OPTI-NASH)".

Non-alcoholic steato-hepatitis (NASH) is the inflammatory, progressive form of non-alcoholic fatty liver disease (NAFLD). A delayed diagnose interval is typical for the majority of the patients because of the asymptomatic natural course. However, serious sequelae may develop such as cirrhosis or hepatocellular carcinoma. NASH is also associated with an increased risk of metabolic diseases. Obesity developed due to a lack of exercise or a disadvantageous diet often leads to NAFLD or NASH, thereby interventions including enhanced physical activity and calorie reduction form the actual gold standard of treatment. To date, patients rarely use these. The project aims to model lifestyle interventions based on the preferences of the NASH patients. Based on a systematic review and focus group discussions, two discrete choice experiments (DCE) will be designed, one on aspects influencing successful uptake of lifestyle interventions and one to analyses parameters contributing to long-term participation. An online survey will be used to elicit patient's preferences on program design and on motivational aspects in a cross-sectional design. The recruitment will take place in nine certified specialist practices and hospital outpatient clinics aiming to reach a sample size of n = 500 which is also required for the DCE design. The results will provide an overview of the NASH patient's preferences regarding the successful uptake and long-term implementation of lifestyle interventions. Recommendations for optimized lifestyle change programs will be derived and an intervention manual will be developed to facilitate target group-specific inclusion in programs in practice.

Integration of a choice modeling approach with immersive virtual environments for accurate space utilization prediction

Optimizing space utilization in buildings is necessary to achieve a sustainable built environment. To this end, accurate space utilization prediction, considering the spatial preference of occupants, is required to design well-utilized spaces in architecture, engineering, and construction projects. However, there is no existing method for quantitatively estimating occupants’ spatial preferences during space planning and design phases to optimize post-occupancy space utilization. Thus, in this study, an immersive choice modeling approach (ICMA) was developed to estimate the spatial preferences of occupants by integrating a discrete choice modeling approach with an immersive virtual environment simulation. The ICMA quantitatively estimates the spatial preferences of occupants in three steps: 1) discrete choice experiment design, 2) immersive virtual environment choice simulation creation, and 3) spatial preference modeling. The ICMA was successfully demonstrated by estimating the spatial preferences of 45 participants for an office. The application of an ICMA was then illustrated through a case example, highlighting its ability to predict space utilization in space planning and design phases.

COVID-19 and online teaching in higher education: A discrete choice experiment

The Sars-Cov-2 coronavirus outbreak significantly impacted Ghana’s educational system, driving schools to close campuses and swiftly deploy online instruction. This study evaluated e-teaching in higher education amidst the Sars-Cov-2 coronavirus by using the University of Ghana as a case study. Specifically, the study investigated university instructors’ preferences for online instructional strategies to enable higher educational institutions to transit smoothly into online teaching and learning. With the help of a face-to-face questionnaire administration, this cross-sectional study used a discrete choice experiment design to capture the responses of 230-course instructors. The analysis of the survey data obtained was possible using the multinomial logit model. Our results revealed that a recorded lecture video had the highest preference among the course instructors, breakdown of teaching content for approximately 30 to 45 minutes, providing online tutorials, and online support/video tutoring from teaching assistants were the important instructional attributes to help higher educational institutions transition into online teaching and learning.

Taking the Shortcut: Simplifying Heuristics in Discrete Choice Experiments.

Health-related discrete choice experiments (DCEs) information can be used to inform decision-making on the development, authorisation, reimbursement and marketing of drugs and devices as well as treatments in clinical practice. Discrete choice experiment is a stated preference method based on random utility theory (RUT), which imposes strong assumptions on respondent choice behaviour. However, respondents may use choice processes that do not adhere to the normative rationality assumptions implied by RUT, applying simplifying decision rules that are more selective in the amount and type of processed information (i.e., simplifying heuristics). An overview of commonly detected simplifying heuristics in health-related DCEs is lacking, making it unclear how to identify and deal with these heuristics; more specifically, how researchers might alter DCE design and modelling strategies to accommodate for the effects of heuristics. Therefore, the aim of this paper is three-fold: (1) provide an overview of common simplifying heuristics in health-related DCEs, (2) describe how choice task design and context as well as target population selection might impact the use of heuristics, (3) outline DCE design strategies that recognise the use of simplifying heuristics and develop modelling strategies to demonstrate the detection and impact of simplifying heuristics in DCE study outcomes.

Factors driving teacher selection on online language tutoring platforms: an experiment-based approach

ABSTRACT Facilitated by the increased availability of affordable broadband Internet, individuals around the world are taking language lessons online from private tutors. A large proportion of online language tutoring takes place through online teaching platforms (OTPs), which are two-sided online markets that connect individual learners and tutors for piece-meal lessons. This paper considers how salient aspects of teachers’ identities drives teacher selection on OTPs. Using a discrete choice experiment design (N = 971) distributed to online English learners from four countries, (Brazil, Italy, Spain and Korea) the paper tests hypotheses related to linguistic, racial and gender-based discrimination. The results reveal that participants’ preference for L1 teachers far exceeds their preference for pedagogically qualified instructors and that learners prefer female to male teachers. Further, learners’ preference for L1 teachers is stronger when the teacher is White than when the teacher is Black. The results also indicate that foreign media consumption correlates with reduced racial bias. Implications and suggestions for further research are discussed.

Urban Vietnamese consumers’ preferences for attributes of sustainably produced rice

PurposeThe purpose of the present research is to examine the comparative values that urban Vietnamese consumers place on attributes of rice that is produced using environmentally friendly methods. The authors consider the impacts that this may have on the livelihoods of rural Vietnamese small farmers. Rice is an “impure public good” that includes both “private” and “public” attributes that consumers consider in their purchase decisions. Consumers make tradeoffs between environmentally and socially beneficial practices (public goods) and perceptions of product quality (private goods). The authors used latent class modeling to investigate the values associated with attributes of rice that is produced using sustainable farming practices.Design/methodology/approachThe authors used a discrete choice experimental design in which consumers stated their choices among combinations of rice attributes. The survey provided responses from 360 urban Vietnamese consumers and allowed to estimate the preferences and nonpecuniary values for rice grown using different levels of environmentally beneficial production methods.FindingsThe results identify two segments of rice consumers: one group of consumers who are sensitive to price and the other group who are sensitive to environmental issues. The individual characteristics are reflected in the choices of production methods and in the willingness to pay for environmentally beneficial outcomes of those methods.Research limitations/implicationsGiven the number of independent variables measured, the sample was relatively small, such that confirmatory statistical methods were inconclusive. However, the authors used multiple analytical tools that provide corroboration of the significant determinants of the utility functions for the two segments.Practical implicationsThe results provide directions for production of rice at a national level, as well as practical implications for consumer-oriented communications.Social implicationsResults suggest that the emerging middle class of Vietnamese consumers are willing to pay more for rice that is produced using methods that are beneficial to the environment. Results also indicate challenges to provide sustainably-produced rice to poorer groups of consumers.Originality/valueThe study provides important context for consumer preferences within emerging economies. This also adds to a growing literature that uses the choice experiment method to estimate consumer valuation of the outcomes of various agricultural practices.

Preference and Willingness to Pay for the Regular COVID-19 Booster Shot in the Vietnamese Population: Theory-Driven Discrete Choice Experiment

The COVID-19 booster vaccination rate has declined despite the wide availability of vaccines. As COVID-19 is becoming endemic and charges for regular booster vaccination are being introduced, measuring public acceptance and the willingness to pay for regular COVID-19 boosters is ever more crucial. This study aims to (1) investigate public acceptance for regular COVID-19 boosters, (2) assess the willingness to pay for a COVID-19 booster shot, and (3) identify factors associated with vaccine hesitancy. Our results will provide crucial insights into and implications for policy response as well as the development of a feasible and effective vaccination campaign during Vietnam's waning vaccine immunity period. A cross-sectional study was conducted among 871 Vietnamese online participants from April to August 2022. An online questionnaire based on the discrete choice experiment (DCE) design was developed, distributed using the snowball sampling method, and subsequently conjointly analyzed on the Qualtrics platform. A history of COVID-19 infection and vaccination, health status, willingness to vaccinate, willingness to pay, and other factors were examined. Among the participants, 761 (87.4%) had received or were waiting for a COVID-19 booster shot. However, the willingness to pay was low at US $8.02, and most participants indicated an unwillingness to pay (n=225, 25.8%) or a willingness to pay for only half of the vaccine costs (n=222, 25.4%). Although information insufficiency and a wariness toward vaccines were factors most associated with the unwillingness to pay, long-term side effects, immunity duration, and mortality rate were the attributes the participants were most concerned with during the vaccine decision-making period. Participants who had children less than 18 years old in their homes infected with COVID-19 had a lower willingness to pay (odds ratio [OR] 0.54, 95% CI 0.39-0.74). Respondents who had children under 12 years old in their family who received at least 1 vaccine dose had a higher willingness to pay (OR 2.03, 95% CI 1.12-3.66). The burden of medical expenses (OR 0.33, 95% CI 0.25-0.45) and fear of the vaccine (OR 0.93, 95% CI 0.86-1.00) were negative factors associated with the level of willingness to pay. A significant inconsistency between high acceptance and a low willingness to pay underscores the role of vaccine information and public trust. In addition to raising awareness about the most concerning characteristics of the COVID-19 booster, social media and social listening should be used in collaboration with health professionals to establish a 2-way information exchange. Work incentives and suitable mandates should continue to encourage workforce participation. Most importantly, all interventions should be conducted with informational transparency to strengthen trust between the public and authorities.

Testing the sensitivity of stated environmental preferences to variations in choice architecture

We conducted a three-way split sample discrete choice experiment (DCE) to investigate welfare estimates for attributes related to the management of coastal cod stocks in Arctic Norway. In a base DCE design, respondents faced three core attributes: (1) coastal cod spawning biomass as an indicator of the sustainability of the cod stocks, (2) stricter regulations on primary user groups (commercial fishers, local recreational anglers, the marine fishing tourism industry), and (3) annual household cost. In two experimentally varied DCE designs, respondents received a fourth attribute that explicitly describes the expansion of the marine fishing tourism industry in the region. In treatment 1, the expansion is represented by the number of coastal cod caught by marine fishing tourists as an indicator of the industry's environmental impact. In treatment 2, the expansion is represented by the number of new jobs as an indicator of the industry's socioeconomic impact. These two attribute translations, designed to be perfectly correlated, serve as an instrument for testing a choice architecture - value activation framework recently proposed in the management science literature. Mixed logit estimation results indicate that welfare estimates vary significantly across choice architectures, both statistically and economically. Additional regression analyses of conditional welfare estimates and respondents' status quo choices yield mixed evidence of value activation. The overall message of the study is that DCE researchers should be cognizant of their role as choice architects when advising public resource managers and policymakers.